Learning data discretization via convex optimization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00318220" target="_blank" >RIV/68407700:21230/18:00318220 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/article/10.1007/s10994-017-5654-4" target="_blank" >http://link.springer.com/article/10.1007/s10994-017-5654-4</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10994-017-5654-4" target="_blank" >10.1007/s10994-017-5654-4</a>
Alternative languages
Result language
angličtina
Original language name
Learning data discretization via convex optimization
Original language description
Discretization of continuous input functions into piecewise constant or piecewise linear approximations is needed in many mathematical modeling problems. It has been shown that choosing the length of the piecewise segments adaptively based on data samples leads to improved accuracy of the subsequent processing such as classification. Traditional approaches are often tied to a particular classification model which results in local greedy optimization of a criterion function. This paper proposes a technique for learning the discretization parameters along with the parameters of a decision function in a convex optimization of the true objective. The general formulation is applicable to a wide range of learning problems. Empirical evaluation demonstrates that the proposed convex algorithms yield models with fewer number of parameters with comparable or better accuracy than the existing methods.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA16-05872S" target="_blank" >GA16-05872S: Probabilistic Graphical Models and Deep Learning</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Machine Learning
ISSN
0885-6125
e-ISSN
1573-0565
Volume of the periodical
107
Issue of the periodical within the volume
2
Country of publishing house
US - UNITED STATES
Number of pages
23
Pages from-to
333-355
UT code for WoS article
000423385500002
EID of the result in the Scopus database
2-s2.0-85023205105